Complex Survey Data¶
CLARITE provides preliminary support for handling complex survey designs, similar to how the r-package survey works.
A SurveyDesignSpec can be created, which is used to obtain survey design objects for specific variables:
sd_discovery = clarite.survey.SurveyDesignSpec(survey_df=survey_design_discovery, strata="SDMVSTRA", cluster="SDMVPSU", nest=True, weights=weights_discovery, single_cluster='scaled')
In the current version of CLARITE, both strata and cluster must be provided. ‘Weights’ are optional, and are expected to be expansion weights.
There are a few different options for the ‘single_cluster’ parameter, which controls how strata with single clusters are handled in the linearized covariance calculation:
error - Throw an error
scaled - Use the average value of other strata
centered - Use the average of all observations
certainty - Single-cluster strata don’t contribute to the variance
When calling the ewas function, the ‘cov_method’ may be set to ‘jackknife’ instead of the default ‘stata’. The ‘single_cluster’ setting has no effect on jackknife covariance.
After a SurveyDesignSpec is created, it can be passed into the ewas function to utilize the survey design parameters:
ewas_discovery = clarite.analyze.ewas("logBMI", covariates, nhanes_discovery_bin, nhanes_discovery_cat, nhanes_discovery_cont, sd_discovery, cov_method='stata')